1. Field of the Art
The present invention is in the field of energy management, and in particular in the area of market-oriented energy distribution using smart grids. Yet more particularly, the present invention pertains to systems for the active management and trading of energy-related securities and resources via energy exchange markets, and the utilization of energy market information in the adjustment of connected systems and devices with a focus on perceived comfort levels for building inhabitants or other affected users.
2. Discussion of the State of the Art
While a robust electric power grid is widely recognized as a vital infrastructure component of a developed economy, technological progress in the field of electricity grid systems has not kept up with the pace of other important technological fields such as telecommunications. Most of the electric grid infrastructure has been in place for decades, and the basic architecture conceived by Thomas Edison and enhanced by the likes of George Westinghouse and Samuel Insull still prevails. Additionally, the current regulatory scheme in the United States discourages large-scale investment in transmission and distribution infrastructure, with the unfortunate result that the grid is often running near capacity.
A number of techniques have been devised to assist in maintaining grid stability during times of high stress, which normally means peak usage hours but also includes periods during normal usage when part of the grid goes offline, thus reducing the effective capacity of the grid or a region of it. It is commonplace for “peaking generators”, often operated by independent power producers, to be placed online at peak periods to give the grid greater capacity; since periods of high demand tend to lead to high wholesale power prices, the business model of peaking generator operators is premised on operating their generators only when the price that can be obtained is high. Large utilities, desiring to avoid the use of high-priced peaking generators when possible, also routinely participate in demand response programs. In these programs, arrangements are made by independent third parties with large commercial, industrial, or institutional users of power to give control to the third parties over certain electric loads belonging to large users. These third parties make complementary arrangements with electric utilities to provide “negative load” during peak periods, on demand, by shedding some portion of the loads under their control when requested by the utility. Typically the cost to the utility of paying these aggregators of “negawatts” (negative megawatts, or negative load available on demand) is much less than the corresponding costs the utilities pay to peak generators for actual megawatts. That is, the utilities pay for “dispatchable load reduction” instead of for “dispatchable peak generation”, and they do so at a lower rate. This arrangement is attractive to the utilities not only because of the immediate price arbitrage opportunity it presents, but also because, by implementing demand reduction, the utilities are often able to defer expensive capital improvements which might otherwise be necessary to increase the capacity of the grid.
A problem with the current state of the art in demand reduction is that it is only practical, in the art, to incorporate very large users in demand reduction programs. Large commercial and industrial users of electricity tend to use far more power on a per-user basis than small commercial and residential users, so they have both the motive (large savings) and the means (experienced facilities management) to take advantage of the financial rewards offered by participation in demand management programs. Additionally, large users of electricity already are accustomed to paying a price for power that depends on market conditions and varies throughout the day, and they often have already invested in advanced building automation systems to help reduce the cost of electricity by conserving.
Unfortunately, a large portion (roughly 33%) of the electric power used during peak periods goes to small users, who do not normally participate in demand management. These users often are unaware of their energy usage habits, and they rarely pay for electricity at varying rates. Rather, they pay a price per unit of electricity used that is tightly regulated and fixed. Partly this is due to the fact that the large majority of small businesses and homes do not have “smart meters”; the amount of power used by these consumers of electricity is measured only once per month and thus there is no way to charge an interval price (typically pricing is set at intervals of 15 minutes when interval pricing is in effect) that varies based on market conditions. Furthermore, the loads in the homes and businesses of small electricity users are invisible to the utilities; it is generally not possible for utilities to “see”, much less to control, loads in homes and small businesses. Loads here refers to anything that uses electricity, including but not limited to lighting, heating ventilation and air conditioning (HVAC), hot water, “white goods” (large appliances such as washers, driers, refrigerators and the like), hot tubs, computers, and so forth.
One approach in the art to improving the situation with small users is to install smart meters at homes small businesses. While the primary motivation for doing so is to enable interval-based usage measurement and the communication of interval-based prices to the users, it is also possible to provide the consumer with much more information on how she uses energy than was possible without a smart meter. Given this granular usage information, utilities and some third parties also hope to be able to send signals, either via pricing or “code red” messages (which ask consumers to turn off unnecessary loads due to grid constraints), or both. In some cases, third parties seek to provide visibility and control to utilities so that, when consumers allow it, the utilities can turn loads off during peak demand to manage the peak. A related method involves the use of “gateway” devices to access a consumer's (again, referring to residences, businesses, and institutions) home area networks (HAN) to communicate with or turn off local devices.
It is a disadvantage of the techniques known in the art that the consumers and small businesses are not, in general, provided with any substantial financial incentives to participate in demand reduction programs (other than merely by saving because they use less power). The “virtual power provider” generally sells “negawatts” as previously described by aggregating demand response capability of many small users and selling demand response services to the utility. This method similarly discourages consumer participation, because the majority of the financial rewards associated with the demand response are not generally passed along to the consumer. The companies that aggregate demand typically charge utilities for the peak reduction, but the consumer is unable to sell their available “negawatts” directly to a utility. This is problematic because this methodology reduces consumer incentives to participate in demand side management, which is a necessary component of modern grid management. And adoption is hampered by the general lack of willingness on the part of consumers to allow utilities to control significant portions of their electricity usage with the consumer having little “say” in the matter. And, from the utilities' point of view, the large variations in consumer usage patterns means that it is much harder for utilities to gage how much demand reduction is enough, in advance; compared to large, stable users such as large office buildings or industrial facilities, utilities face a complex mix of user patterns that are difficult to predict and virtually impossible to control. As a result, at the present time almost no demand reduction takes place among consumers and small business users of the electric grid.
Another problem in the art today is the incorporation of distributed generation and storage systems, which are proliferating, into grid demand management systems. In many cases, consumers are unable to do more than to offset their own electric bills with generation units (such as microturbines powered by wind, or solar panels on a roof, or plug-in electric hybrid vehicles that could add energy to the grid when needed), because utilities have neither the means nor the motivation to pay them for the extra electricity they generate. Many states require utilities to buy excess power generated; but, without an ability to sell that generated power at a price that represents a more holistic view of its value that includes “embedded benefits” (i.e. at a rate that may consider, but is not limited to, the effect on enhancing local power quality, proximity to loads, type of power generated and the associated reduction in carbon and other negative externalities—like sulfur dioxide and nitrogen dioxide—and the reduced capital costs resulting from the reduction of required capital investments in infrastructure), most distributed power generation remains economically unfeasible, to the detriment of all parties. With the growing number of markets associated with trading negative externalities associated with electrical power generation (most prominently including carbon, but also nitrogen dioxide and sulfur dioxide), it is necessary to fully account for the value of such energy sources and storage options, and to ensure that double counting of environmental benefits that are related to the generation and distribution of the electricity itself is not conducted. Sulfur dioxide and nitrogen dioxide became regulated in the U.S. under the 1990 Clean Air Act Amendments, which established the EPA's Acid Rain Program to implement a cap-and-trade method to reduce harmful emissions from the electric power industry. Additionally, while storage units may allow users to avoid peak charges and to even the flow of locally generated power (for instance, by storing wind power during high wind conditions and returning it when the wind conditions are low), it is generally not possible for users to sell stored power to the grid operator at its true value for the same reasons.
An additional challenge associated with integrating distribute energy resources with the grid is the lack of a cost-effective means of aggregating distributed power generation into a form that can be traded in a manner similar to the large blocks of power that are bought and sold by more traditional commercial power plants like coal and nuclear. Complex industry rules discourage participation and even consolidators have been hesitant to enter the market given the high set up costs associated with communications, staffing, and industry monitoring. A mechanism is needed to enable equal participation of distributed energy generators (e.g. solar panels on the roof of a home) and traditional power generators in order to encourage the development of these resources.
An underlying difficulty that contributes to the problems already described is that consumers (commercial, industrial, institutional, or residential participants in energy markets) have no way to differentiate between one unit of energy and another in energy distribution systems, such as the electric grid, that are best viewed as “continuous-flow energy networks”. This type of network can be contrasted with “discrete- or packet-flow energy distribution networks” such as the coal distribution system. The global oil distribution network is a good example of a hybrid, or mixed, energy distribution network that uses both discrete-flow and continuous-flow techniques at various points in the network. With continuous-flow energy distribution networks such as the electric power distribution system (or grid) and the natural gas distribution system, the units of energy are indistinguishable physically, one from another, at the point of consumption. That is, a consumer cannot differentiate one kilowatt of electricity arriving at her home or business from another, and in general has no ability to differentiate between energy having desirable qualities (to her) such as renewability, low carbon footprint, derivation from local or at least domestic (as opposed to foreign) sources, and so forth. Since the physical properties of electricity or natural gas are essentially fixed and do not vary based on the source, the only attributes consumers can know are quantity and price. While in some cases utilities make available about information about the aggregate sources of their electricity, and while they may in some cases make a small number of “packages” available to consumers based on differing mixes of sources (for instance, “black, green and in between” menu choices based on percentage of renewable or low-carbon sources for each option, with prices varying accordingly), it is in general true that consumers have no information about the particular energy they are using at any given time, and no ability to make informed choices as energy consumers.
Today's energy distribution networks are “information-poor” and treat energy as a commodity that is only differentiated by price. What is needed is an “information-rich” energy distribution network.
One approach to addressing the “information-poor” nature of current distribution systems that provide energy to consumers (taken herein to mean residential, industrial, institutional, and commercial consumers of energy) is “smart metering”. Smart meters are a natural extension of the well-established electricity meters that today measure electricity usage at virtually all consumer locations. Under the older (pre-smart meter) system of measuring electricity usage, human meter readers would physically go at regular, long intervals (monthly or bi-monthly, generally) and read a current value, typically in kilowatt-hours, of total energy consumption at that site since the meter last “rolled over” (passed its maximum reading and started over at zero). This new value would have the previous value subtracted from it to give the energy used in the period since the last meter reading. There are two main problems with the older meter system: first, meter readers are expensive; second, because readings can only practically be taken at long intervals, there is no way for utilities to measure usage specifically during particular time intervals such as a peak hour. Without the ability to make readings at frequent intervals (a common desired target is to have fifteen-minute readings), it is practically impossible for utilities to offer or impose demand-based pricing schemes, for instance where electricity prices are set higher during periods of peak demand. For very large consumers, utilities and the consumers have found common ground and the consumers have allowed sophisticated measurement systems to be put in place (or have done it themselves), and have switched to demand-based pricing; these large consumers typically have building automation and energy control systems that allow them to manage energy usage and to avoid excessive usage during peak periods. By switching to demand-based pricing, these consumers get a lower overall energy bill because prices during periods of low demand are typically much lower than the fixed prices used in non-demand-based pricing schemes (usually these prices are set as fixed tariffs and reflect an average of peak and low usage prices that would have been charged in demand-based pricing schemes).
While to some extent the problem of obtaining frequent usage readings has been solved for very large consumers, the situation is very different for residential and small commercial users, who collectively account for approximately 50% of electricity usage in the United States. A solution that is currently favored by the utility industry as a whole is to gradually shift the entire user base to “smart meters”, which are energy meters that are connected via a data network to the utility and are able to take readings at arbitrary time intervals under the control of the utility. Deployment of smart meters, among other things, makes it possible for utilities to implement demand-based pricing schedules for all consumers served by smart meters, which is extremely important for utilities and consumers alike (as demand-based pricing should help to control demand especially at peak periods). But the cost of deploying smart meters is quite high, typically reaching several hundred dollars per installed smart meter. With tens of millions of ratepayers in the United States alone, switching completely to smart meters will likely cost many billions of dollars, and it will take a considerable period of time.
Besides their high costs, smart meters suffer from another disadvantage, albeit one which would not trouble utilities themselves. Since smart meters are being deployed exclusively by utilities in the United States (since it has always been the responsibility of the utilities to install, maintain, and own usage meters), widespread deployment of smart meters will tend to lock in consumers with their local utility. This situation, which prevails today, is in sharp contrast to the situation in the telecommunications industry, where many consumers have a choice of carriers, even for local service. If real-time markets are not developed in parallel with smart meter deployments, smart meter deployment will reinforce utilities' stranglehold on their consumer base, which may not serve the best interests of consumers or the economy as a whole. If developed in parallel, smart meter deployments and parallel market-based network management can have many synergistic effects.
Another aspect of the problem of energy management in more market-oriented, information-rich scenarios is the determination and management of risk. There are several relevant areas of risk that must be considered by market participants. These include familiar risks such as the creditworthiness of counterparties in energy transactions, but these familiar risks are taken into unfamiliar territory when large numbers of less sophisticated market participants are considered (such as where small businesses and residences participate in demand response management programs or contribute power to the grid for distributed energy sources). Other types of potentially relevant risks are new, including such novel risks as the risk that, when large numbers of small participants elect to respond to a demand response management signal, their geographic distribution creates stability problems on the grid. In order for efficient markets that combine both demand response and distributed energy generation to be possible, and to be attractive to prospective market participants, the overall risk profiles of participants and of the derivative energy securities traded on such markets must be visible and must maintain the confidence of these participants. Furthermore, development of real-time energy markets requires that uncertainty and variability of loads and sources on the network be quantitatively and qualitatively transparent and manageable through tradable financial and physical trading rights. As markets continue to develop into more effective tools to integrate increasingly large numbers of participants, two types of risk must be simultaneously managed in market-based smart grid solutions: financial risk and system operations risk. This is a distinct challenge compared to the purely financial risks that are commonly measured and allocated in financial derivatives.
In addition to the practical challenges associated with integration of large quantities of renewable energy resources and distributed energy resources (generation and storage), the energy markets must have tools to effectively price the effect of infrastructure reliability on the network due to the physical limitations of the network to deliver electricity to end-users. This requires that reliability ratings for actual physical infrastructure assets can also be described qualitatively and quantitatively for inclusion in real-time markets and futures markets for energy derivatives. The scoring of infrastructure reliability is an important part of quantifying system operations risk inherent in the system that must be accounted for in financial models if risk is to be allocated in an appropriate and socially optimal manner.
In addition to challenges in management of the grid, the existing energy market structure results in inefficient pricing and taxation of market externalities. The inability to effectively attribute system losses (e.g. transmission losses) to network/market participants stems from the current inability to facilitate nodal allocation of energy on continuous flow energy networks (that is, allocation of real costs and externalities associated with energy transformations on a node-by-node basis, for instance by assigning a carbon cost to electricity losses on a high-voltage transmission line and then allocating that cost to each user of electricity which was transmitted along that transmission line). In a continuous flow energy networks with proper energy information overlay networks, it is possible to effectively attribute the negative externalities of power generation, transmission, distribution, and storage to end consumers with particularity, such that the end-to-end environmental effects of energy usage can be quantified. Once quantified and attributed to end consumers, more effective means of pricing pollution and other negative externalities can be explored by government beyond methods such as cap-and-trade that are currently being considered. With end-to-end accountability it is possible to tax pollution in the final goods and services produced directly, which increases transparency and affects consumer behavior in order to help reach national or supra-national environmental goals.
Another important aspect of managing energy markets is pricing of derivative energy securities. When considering instruments which consist of aggregated demand energy reduction or distributed energy generation obligations, there are two important financial aspects to consider: the appropriate price for the instrument, and the actual price to be paid to the various entities who voluntarily have committed to carry out certain demand reduction or distributed generation actions on demand in return for financial compensation. The derivative energy securities are similar in nature to commodities futures, in which a price is paid on an open market for the right to buy or sell a certain commodity at a certain price at or by some definite time in the future. The price for the instrument is distinct from, but dependent on, the price of the underlying commodity, and a purchaser of such a commodities future instrument who plans on actually taking (or making) delivery of the commodity has to consider both the price to be paid for the instrument and the ultimate price of the commodity (as compared to the market price at the time of the settling of the contract) to determine whether or not to proceed with a purchase (or sale) of such a futures contract (or financial instrument). But in commodities futures, the actual delivery of commodities on settlement of a contract is not facilitated or managed by the market or exchange that handled transactions involving the futures contract; what is traded on such exchanges are contractual obligations only. Parties to final contracts for delivery and receipt of contracts have resort to legal mechanisms in the case of failures of counterparties to fulfill their obligations, without the involvement of the exchange that made the market in the futures contracts. In situations where exchanges may actually involve themselves in the delivery of the underlying physical assets being traded, and may take on a certain measure of risk with regard to such deliveries, the pricing of futures contracts becomes more complicated as there may be at least three parties bearing some measure of risk associated with each contract: a buyer, a seller, and an exchange.
As the electric grid system continues to integrate increasing numbers of diverse market participants, the markets which determine the relationship between parties interacting on the network will continue to become ever more important. In fact, future reliability is likely to be provided through market operations, and not the decisions or actions of central planners. The development of complex energy markets capable of effectively managing operation of the grid within the physical, operational, and policy constraints required will require the development and implementation of new trading tools to interact with the markets.
At the core of these new markets will be the trading platforms that provide new ways to integrate engineering and business decisions to manage the physical and financial risks that, in the case of the electric grid, are coupled due to the unique constraints of the network. In order to manage the diverse energy resources, spiking demand, and continuously increasing uncertainty and variability, all market participants require new tools to interact with developing markets that enable them to effectively manage physical and financial risk.
Current methods for managing the utilization of network assets (including, for example, transmission and distribution network lines) are suboptimal. This is due to lack of visibility to most players regarding line losses (which are often as much as 8-10% of electricity generated) and system requirements such as ancillary services, both of which remain unattributed to end-users. For example, voltage support required for a large factory is not attributed to the factory in any meaningful way, leading to a free-rider problem. Although Supervisory Control and Data Acquisition (or “SCADA”) systems often provide extremely low-latency data feeds to utility network operations centers (or “NOCs”), these systems are often not configured in a manner that enables wide area network optimization and utilization to occur. The use of transmission and distribution assets across many service areas by many different physical and energy asset holders is critical to providing more transparent and efficient markets that are capable of meeting the energy challenges faced by the United States and many other nations.
Challenges with the pricing and trading of transmission rights, in many cases, are linked to the fact that electrons flow over all parallel paths inversely proportional to the impedance of the path, and do not follow any “specified” contractual path (as assumed in physical transmission rights). Loop flow becomes a problem when bilateral transactions between market participants extend to cause flow problems in third-party systems (i.e. other parts of the network). More generically, this problem is a direct result of the inability to directly control the flows between any two points on the network within the grid network without influencing line flows elsewhere on the system. In short, simplified “shortest distance” contract paths across the electric grid network are neither technically nor economically efficient. It is this highly interconnected nature of the electric grid system that requires new approaches for managing dispatch, network utilization, and network optimization.
It is common in the art to base the entire operation of the grid on the concept of control areas that are premised on the concept that loads and resources will maintain an instantaneous balance across the network. This assumption of uniform power quality and instantaneous balancing of energy assets on the network is not necessarily useful. Control Area concepts are not required for efficient and reliable provision of services and management of the electric grid. In fact, the assumption that all users require the same power quality and consistency of access is in some cases detrimental to parts of society who may be unable to pay for such services or end up paying for such services without understanding the true sources of the associated costs. It is also common in the art to utilize Area Control Error (ACE) as the metric with which to manage control areas. This is generally specified by a standards or regulatory agency that tells system operators to keep their ACE within tight limits in two key ways: first, that the control area balance (on an instantaneous power basis) is kept in balance with the rest of the interconnection at least once every ten minutes and second, that the control area's energy imbalance remain within a specified limit (generally 0.2-0.4% of peak demand) every ten minutes. The control area concept and many of the centralized dispatch and scheduling mechanisms utilized today introduce large inefficiencies into the system and provide problems for the provision of effective low cost energy services due to scheduling and dispatch limitations associated with fixed scheduling mechanisms. New systems and methods that are able to provide effective tools for customers to purchase electricity according to their needs and desires as “services down the wire” are possible if correctly implemented.
Another challenge of energy is that the end consumer requires energy to run their home or business. Rarely do they have an understanding of the complexities of how energy pricing works. Instead, they focus on how to generate revenue or control the costs of their own business or residence. End users are primarily interested in the end-state and benefit that energy enables. For instance, end users value the delivery of comfort, convenience, mobility, security, among other tangible economic benefits. The state-of-the-art of energy management lacks a translation mechanism of turning the complexities of energy into tangible, physical experiences As an example, the value of comfort can be delivered by adjusting the control system of a central heating system, boiler, space heater, sunlight etc. Users lack an ability to optimize their total comfort based on economic variables.
It is an object of the present invention to utilize a system and method for enabling nodal allocation of electricity and the effective utilization and optimization of transmission and distribution infrastructure of the electric grid system, such that intelligent adjustments may be made to connected devices and systems to optimize perceived comfort levels.